Environmental Kuznets Curve Hypothesis in Sub-Saharan African Countries: Evidence from Panel Data Analysis. (Published)
The paper investigates the validity of Environmental Kuznets Curve (EKC) hypothesis in Sub-Saharan African countries, using panel data analysis for the period 1980 to 2012. It seeks to examine the effects of economic growth on the environmental quality of the countries in the sub-region. The paper used annual secondary time series data obtained from World Bank Development Indicators (WDI) for the period under review. The validity of EKC hypothesis was examined through panel data fixed and random effects analysis. Panel unit root test and panel cointegration test were conducted to determine the degree of stationarity of the variables and long–run relationship among our variables of interest respectively. The paper considers a variety of pollutants in estimating the EKC pattern with observation that the responses of EKC depend largely on the nature of pollutants. The results of the empirical analysis support the validity of the EKC hypothesis for solid emission (CSF) and composite factor of emission (CFE) but maintain non-existence of EKC hypothesis for other pollutants: carbon emission (CO2), industrial emission (CIN) and liquid emission (CLQ). The paper confirms that not all the selected environmental pollutants follow the same EKC process in the selected countries of Sub-Saharan African (SSA). The paper recommends that the impression of “polluting the economy now as it clears itself later” does not hold for many of the pollutants and should not be adopted in the region so as to ensure better environmental quality as there is no proof of better quality if it is not regulated. The findings show that SSA countries need to harmonise a well – coordinated environmental and economic policy mix that would ensure greater output but at the same time protect their environment from degradation and pollution
Keywords: Cointegration, Environmental Kuznets Curve (EKC), Fixed and Random Effects., Panel Data Analysis
ENVIRONMENTAL KUZNETS CURVE HYPOTHESIS IN SUB-SAHARAN AFRICAN COUNTRIES- EVIDENCE FROM PANEL DATA ANALYSIS. (Published)
The paper investigates the validity of Environmental Kuznets Curve (EKC) hypothesis in Sub-Saharan African countries, using panel data analysis for the period 1980 to 2012. It seeks to examine the effects of economic growth on the environmental quality of the countries in the sub-region. The paper used annual secondary time series data obtained from World Bank Development Indicators (WDI) for the period under review. The validity of EKC hypothesis was examined through panel data fixed and random effects analysis. Panel unit root test and panel cointegration test were conducted to determine the degree of stationarity of the variables and long–run relationship among our variables of interest respectively. The paper considers a variety of pollutants in estimating the EKC pattern with observation that the responses of EKC depend largely on the nature of pollutants. The results of the empirical analysis support the validity of the EKC hypothesis for solid emission (CSF) and composite factor of emission (CFE) but maintain non-existence of EKC hypothesis for other pollutants: carbon emission (CO2), industrial emission (CIN) and liquid emission (CLQ). The paper confirms that not all the selected environmental pollutants follow the same EKC process in the selected countries of Sub-Saharan African (SSA). The paper recommends that the impression of “polluting the economy now as it clears itself later” does not hold for many of the pollutants and should not be adopted in the region so as to ensure better environmental quality as there is no proof of better quality if it is not regulated. The findings show that SSA countries need to harmonise a well – coordinated environmental and economic policy mix that would ensure greater output but at the same time protect their environment from degradation and pollution.
Keywords: Cointegration, Environmental Kuznets Curve (EKC), Fixed and Random Effects., Panel Data Analysis